Process control systems, such as distributed or scalable process control systems like those used in chemical, petroleum or other processes, typically include one or more process controllers communicatively coupled to each other, to at least one host or operator workstation and to one or more field devices via analog, digital or combined analog/digital buses. The field devices, which may be, for example, valves, valve positioners, switches and transmitters (e.g., temperature, pressure and flow rate sensors), perform functions within the process such as opening or closing valves and measuring process parameters. The process controller receives signals indicative of process measurements made by the field devices and/or other of information pertaining to the field devices, and uses this information to implement a control routine and then generates control signals which are sent over the buses to the field devices to control the operation of the process. Information from the field devices and the controller is typically made available to one or more applications executed by the operator workstation to enable an operator to perform any desired function with respect to the process, such as viewing the current state of the process, modifying the operation of the process, etc.
Some process control systems, such as the DeltaV® system sold by Fisher Rosemount Systems, Inc., headquartered in Austin, Tex., use function blocks or groups of function blocks referred to as modules located in the controller or in different field devices to perform control operations. In these cases, the controller or other device is capable of including and executing one or more function blocks or modules, each of which receives inputs from and/or provides outputs to other function blocks (either within the same device or within different devices), and performs some process operation, such as measuring or detecting a process parameter, controlling a device, or performing a control operation, such as the implementation of a proportional-integral-derivative (PID) control routine. The different function blocks and modules within a process control system are generally configured to communicate with each other (e.g., over a bus) to form one or more process control loops.
Process controllers are typically programmed to execute a different algorithm, sub-routine or control loop (which are all control routines) for each of a number of different loops defined for, or contained within a process, such as flow control loops, temperature control loops, pressure control loops, etc. Generally speaking, each such control loop includes one or more input blocks, such as an analog input (AI) function block, a single-output control block, such as a proportional-integral-derivative (PID) or a fuzzy logic control (FLC) function block, and an output block, such as an analog output (AO) function block.
Control routines, and the function blocks that implement such routines, have been configured in accordance with a number of control techniques, including PID control, fuzzy logic control, and model-based techniques such as a Smith Predictor or Model Predictive control (MPC). In model-based control techniques, the parameters used in the routines to determine the closed loop control response are based on the dynamic process response to changes in the manipulated or measured disturbances serving as inputs to the process. A representation of this response of the process to changes in process inputs may be characterized as a process model. For instance, a first-order parameterized process model may specify values for the gain, dead time, and time constant of a self-regulating process or values for integrating gain and dead time of an integrating process.
One model-based technique, model predictive control (MPC), involves a number of step or impulse response models designed to capture the dynamic relationships between process inputs and outputs. With MPC techniques, the process model is directly used to generate the controller. When used in connection with processes that experience large changes in process dead time, process delay, etc., the MPC controller must be automatically regenerated using the models to match the current process condition. In such cases, a process model was accordingly identified at each of a number of operating conditions. The introduction of multiple process models and the requisite automatic generation of the controller to matching the current process condition undesirably increased the complexity of the process control system.
Process models have also been used to set tuning parameters of PID and other control schemes using adaptive control techniques, where the tuning of the PID (or other) controller is generally updated as a result of changes in the process model and a user-selected tuning rule. See, e.g., U.S. Pat. Publication No. 2003/0195641 entitled “State Based Adaptive Feedback Feedforward PID Controller” and U.S. Pat. No. 6,577,908 entitled “Adaptive Feedback/Feedforward PID Controller,” the entire disclosures of which are hereby expressly incorporated by reference herein.
A process control system generally has the ability to tune process loops and provide some kind of performance monitoring. However, as conditions change and process equipment degrades the existing tuning results in inefficient operation. While some manufacturers have tried to address this problem by layering complex applications on top of the control system, the continuing sophistication of hardware and software provides the ability to embed more and more functionality in the native control system itself. The next generation intelligent control system provides embedded learning algorithms that constantly observe every loop and every device of the system, thereby enabling intelligent monitoring, diagnostics, and advanced tuning, automatically. The algorithms learn and remember the process models, and, as conditions change, re-learn the process automatically. This information can then be used to evaluate control performance and behavior, and diagnose problems. Finally, the control performance can be improved by advising operators of under performing loops or recommended tuning, and ultimately, by automatic retuning of the loops with continuous adaptive control. An example of automatic process identification in a control system is described in U.S. Pat. Publication No. 2007/0078533 entitled “Process Model Identification In A Process Control System,” the entire disclosure of which is hereby expressly incorporated by reference herein. This combination of intelligent control with intelligent devices realizes a control system with predictive intelligence.